Data Science Training in Oslo

This instructor-led, live training in Oslo (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

By the end of this training, participants will be able to:

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Oslo (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

By the end of this training, participants will be able to:

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led live training in Oslo (online or onsite) is designed for intermediate-level participants who want to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for orchestrating machine learning workflows.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led live training in Oslo (online or onsite) is aimed at beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

Python has surged in popularity within the financial sector, widely adopted by major investment banks and hedge funds to develop a diverse array of financial applications. These range from essential trading systems to sophisticated risk management platforms.

Through this instructor-led live training, participants will acquire the skills needed to utilize Python for developing practical applications that address specific financial challenges.

Upon completion of this training, participants will be capable of:

• Grasping the core principles of the Python programming language
• Downloading, installing, and managing optimal development tools for building financial applications in Python
• Choosing and employing the most effective Python packages and programming techniques to organize, visualize, and analyze financial data from diverse sources (such as CSV, Excel, databases, and web interfaces)
• Creating applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshooting, integrating, deploying, and optimizing Python applications

Audience

• Developers
• Analysts
• Quants

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training focuses on providing solutions for key challenges faced by finance professionals. If there are specific topics, tools, or techniques you wish to include or explore in greater depth, please contact us to arrange it.

Explores practical methodologies for Data Science and AI leveraging Python, empowering professionals to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. The curriculum encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data handling via Spark, and data-driven storytelling. This course is well-suited for beginners pursuing a Python data science certification and aiming for career-ready analytics training.

KNIME Analytics Platform stands out as a premier open-source solution for data-driven innovation, empowering you to uncover hidden potential within your data, extract fresh insights, or forecast future trends. Boasting over 1000 modules, numerous ready-to-run examples, a broad array of integrated tools, and the most extensive selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for data scientists and business analysts alike.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME specialists to familiarize themselves with KNIME, enhance their proficiency in using it, and learn how to produce clear and comprehensive reports using KNIME workflows.

This instructor-led live training (available online or onsite) is designed for data professionals seeking to leverage KNIME to address complex business challenges.

It is specifically targeted at individuals with no programming background who wish to utilize cutting-edge tools to implement analytics scenarios.

Upon completion of this training, participants will be able to:

• Install and configure KNIME.
• Develop Data Science scenarios
• Train, test, and validate models
• Implement the end-to-end value chain of data science models

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course or to learn more about the program, please contact us to make arrangements.

This instructor-led, live training in Oslo (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.